Overview

Dataset statistics

Number of variables20
Number of observations48678
Missing cells54628
Missing cells (%)5.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.4 MiB
Average record size in memory160.0 B

Variable types

Numeric8
Categorical12

Alerts

Rural_Urban is highly correlated with WealthQuantile and 2 other fieldsHigh correlation
BirthOrder is highly correlated with TotalChildrenHigh correlation
TotalChildren is highly correlated with BirthOrderHigh correlation
WealthQuantile is highly correlated with Rural_Urban and 1 other fieldsHigh correlation
Toiletfascility is highly correlated with Rural_UrbanHigh correlation
Place_of_delivery is highly correlated with Rural_UrbanHigh correlation
Mediaexposure is highly correlated with WealthQuantileHigh correlation
Rural_Urban is highly correlated with WealthQuantile and 2 other fieldsHigh correlation
BirthOrder is highly correlated with TotalChildrenHigh correlation
TotalChildren is highly correlated with BirthOrderHigh correlation
WealthQuantile is highly correlated with Rural_Urban and 1 other fieldsHigh correlation
Toiletfascility is highly correlated with Rural_UrbanHigh correlation
Place_of_delivery is highly correlated with Rural_UrbanHigh correlation
Mediaexposure is highly correlated with WealthQuantileHigh correlation
Rural_Urban is highly correlated with Toiletfascility and 1 other fieldsHigh correlation
BirthOrder is highly correlated with TotalChildrenHigh correlation
TotalChildren is highly correlated with BirthOrderHigh correlation
Toiletfascility is highly correlated with Rural_UrbanHigh correlation
Place_of_delivery is highly correlated with Rural_UrbanHigh correlation
Mediaexposure is highly correlated with WealthQuantileHigh correlation
Place_of_delivery is highly correlated with Rural_Urban and 1 other fieldsHigh correlation
Toiletfascility is highly correlated with Rural_Urban and 1 other fieldsHigh correlation
Rural_Urban is highly correlated with Place_of_delivery and 2 other fieldsHigh correlation
WealthQuantile is highly correlated with Mediaexposure and 3 other fieldsHigh correlation
Region is highly correlated with Rural_Urban and 2 other fieldsHigh correlation
Rural_Urban is highly correlated with Region and 5 other fieldsHigh correlation
No_of_Children_under_5 is highly correlated with dead_aliveHigh correlation
BirthOrder is highly correlated with TotalChildrenHigh correlation
TotalChildren is highly correlated with BirthOrderHigh correlation
HighestEducation is highly correlated with Rural_Urban and 3 other fieldsHigh correlation
WealthQuantile is highly correlated with Region and 1 other fieldsHigh correlation
Toiletfascility is highly correlated with Rural_Urban and 2 other fieldsHigh correlation
Place_of_delivery is highly correlated with Region and 3 other fieldsHigh correlation
Mediaexposure is highly correlated with Rural_Urban and 1 other fieldsHigh correlation
age is highly correlated with dead_aliveHigh correlation
dead_alive is highly correlated with No_of_Children_under_5 and 1 other fieldsHigh correlation
Prec.BirthInterval has 9915 (20.4%) missing values Missing
childSize has 5719 (11.7%) missing values Missing
WealthQuantile has 10873 (22.3%) missing values Missing
prev_termin_pregnancy has 5719 (11.7%) missing values Missing
Toiletfascility has 1323 (2.7%) missing values Missing
HandwashFascility has 9232 (19.0%) missing values Missing
Place_of_delivery has 6389 (13.1%) missing values Missing
Mediaexposure has 5458 (11.2%) missing values Missing
No_of_Children_under_5 has 2073 (4.3%) zeros Zeros
age has 2259 (4.6%) zeros Zeros

Reproduction

Analysis started2022-02-14 14:52:20.796553
Analysis finished2022-02-14 14:52:43.249970
Duration22.45 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Region
Real number (ℝ≥0)

HIGH CORRELATION

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.812666913
Minimum1
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.4 KiB
2022-02-14T17:52:43.343969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q37
95-th percentile14
Maximum15
Range14
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.798439528
Coefficient of variation (CV)0.6534762073
Kurtosis-0.02606129977
Mean5.812666913
Median Absolute Deviation (MAD)2
Skewness0.9068475744
Sum282949
Variance14.42814285
MonotonicityNot monotonic
2022-02-14T17:52:43.540994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
48171
16.8%
76857
14.1%
35846
12.0%
14775
9.8%
54502
9.2%
24046
8.3%
63924
8.1%
121972
 
4.1%
131732
 
3.6%
151667
 
3.4%
Other values (5)5186
10.7%
ValueCountFrequency (%)
14775
9.8%
24046
8.3%
35846
12.0%
48171
16.8%
54502
9.2%
63924
8.1%
76857
14.1%
81155
 
2.4%
91046
 
2.1%
10746
 
1.5%
ValueCountFrequency (%)
151667
 
3.4%
141293
 
2.7%
131732
 
3.6%
121972
 
4.1%
11946
 
1.9%
10746
 
1.5%
91046
 
2.1%
81155
 
2.4%
76857
14.1%
63924
8.1%

Rural_Urban
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size380.4 KiB
2
40340 
1
8338 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
240340
82.9%
18338
 
17.1%

Length

2022-02-14T17:52:43.732949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-14T17:52:43.839916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
240340
82.9%
18338
 
17.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

No_of_Children_under_5
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.759439583
Minimum0
Maximum10
Zeros2073
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size380.4 KiB
2022-02-14T17:52:43.934916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q32
95-th percentile3
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8495153355
Coefficient of variation (CV)0.4828329111
Kurtosis1.303616425
Mean1.759439583
Median Absolute Deviation (MAD)1
Skewness0.5019738489
Sum85646
Variance0.7216763053
MonotonicityNot monotonic
2022-02-14T17:52:44.067917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
221671
44.5%
116921
34.8%
36922
 
14.2%
02073
 
4.3%
4899
 
1.8%
5143
 
0.3%
646
 
0.1%
103
 
< 0.1%
ValueCountFrequency (%)
02073
 
4.3%
116921
34.8%
221671
44.5%
36922
 
14.2%
4899
 
1.8%
5143
 
0.3%
646
 
0.1%
103
 
< 0.1%
ValueCountFrequency (%)
103
 
< 0.1%
646
 
0.1%
5143
 
0.3%
4899
 
1.8%
36922
 
14.2%
221671
44.5%
116921
34.8%
02073
 
4.3%

Age_at_1st_Birth
Real number (ℝ≥0)

Distinct34
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.86638728
Minimum8
Maximum44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.4 KiB
2022-02-14T17:52:44.231918image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile14
Q116
median18
Q321
95-th percentile26
Maximum44
Range36
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.692454624
Coefficient of variation (CV)0.1957160409
Kurtosis1.732410874
Mean18.86638728
Median Absolute Deviation (MAD)2
Skewness0.9666448766
Sum918378
Variance13.63422115
MonotonicityNot monotonic
2022-02-14T17:52:44.410952image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
175939
12.2%
185622
11.5%
195493
11.3%
165461
11.2%
204705
9.7%
154189
8.6%
213493
7.2%
222818
5.8%
142197
 
4.5%
231942
 
4.0%
Other values (24)6819
14.0%
ValueCountFrequency (%)
81
 
< 0.1%
92
 
< 0.1%
1014
 
< 0.1%
11119
 
0.2%
12545
 
1.1%
13975
 
2.0%
142197
 
4.5%
154189
8.6%
165461
11.2%
175939
12.2%
ValueCountFrequency (%)
442
 
< 0.1%
407
 
< 0.1%
392
 
< 0.1%
3812
 
< 0.1%
3713
 
< 0.1%
3623
 
< 0.1%
3531
 
0.1%
3439
 
0.1%
3367
0.1%
32116
0.2%

BirthOrder
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.914848597
Minimum1
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.4 KiB
2022-02-14T17:52:44.544739image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q36
95-th percentile9
Maximum18
Range17
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.560884426
Coefficient of variation (CV)0.6541464791
Kurtosis0.148806264
Mean3.914848597
Median Absolute Deviation (MAD)2
Skewness0.8319685542
Sum190567
Variance6.558129043
MonotonicityNot monotonic
2022-02-14T17:52:44.675714image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
19841
20.2%
28330
17.1%
36965
14.3%
45951
12.2%
55099
10.5%
64177
8.6%
73207
 
6.6%
82226
 
4.6%
91378
 
2.8%
10834
 
1.7%
Other values (8)670
 
1.4%
ValueCountFrequency (%)
19841
20.2%
28330
17.1%
36965
14.3%
45951
12.2%
55099
10.5%
64177
8.6%
73207
 
6.6%
82226
 
4.6%
91378
 
2.8%
10834
 
1.7%
ValueCountFrequency (%)
181
 
< 0.1%
171
 
< 0.1%
165
 
< 0.1%
159
 
< 0.1%
1421
 
< 0.1%
1359
 
0.1%
12186
 
0.4%
11388
 
0.8%
10834
1.7%
91378
2.8%

Prec.BirthInterval
Real number (ℝ≥0)

MISSING

Distinct201
Distinct (%)0.5%
Missing9915
Missing (%)20.4%
Infinite0
Infinite (%)0.0%
Mean37.9238965
Minimum7
Maximum258
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.4 KiB
2022-02-14T17:52:44.849176image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile14
Q124
median33
Q345
95-th percentile79
Maximum258
Range251
Interquartile range (IQR)21

Descriptive statistics

Standard deviation21.70359206
Coefficient of variation (CV)0.5722933048
Kurtosis9.435824857
Mean37.9238965
Median Absolute Deviation (MAD)10
Skewness2.333973418
Sum1470044
Variance471.0459082
MonotonicityNot monotonic
2022-02-14T17:52:45.206176image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
251245
 
2.6%
261217
 
2.5%
241156
 
2.4%
231136
 
2.3%
341081
 
2.2%
221080
 
2.2%
271074
 
2.2%
331052
 
2.2%
281049
 
2.2%
291027
 
2.1%
Other values (191)27646
56.8%
(Missing)9915
 
20.4%
ValueCountFrequency (%)
78
 
< 0.1%
832
 
0.1%
9126
 
0.3%
10239
0.5%
11337
0.7%
12412
0.8%
13510
1.0%
14523
1.1%
15453
0.9%
16503
1.0%
ValueCountFrequency (%)
2581
 
< 0.1%
2521
 
< 0.1%
2471
 
< 0.1%
2411
 
< 0.1%
2381
 
< 0.1%
2291
 
< 0.1%
2281
 
< 0.1%
2241
 
< 0.1%
2191
 
< 0.1%
2163
< 0.1%

TotalChildren
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.771375159
Minimum0
Maximum14
Zeros457
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size380.4 KiB
2022-02-14T17:52:45.360305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q35
95-th percentile8
Maximum14
Range14
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.193097519
Coefficient of variation (CV)0.5815113657
Kurtosis0.02821227518
Mean3.771375159
Median Absolute Deviation (MAD)1
Skewness0.696249297
Sum183583
Variance4.809676729
MonotonicityNot monotonic
2022-02-14T17:52:45.490459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
29611
19.7%
38242
16.9%
47277
14.9%
16713
13.8%
55877
12.1%
64353
8.9%
73061
 
6.3%
81723
 
3.5%
9796
 
1.6%
0457
 
0.9%
Other values (5)568
 
1.2%
ValueCountFrequency (%)
0457
 
0.9%
16713
13.8%
29611
19.7%
38242
16.9%
47277
14.9%
55877
12.1%
64353
8.9%
73061
 
6.3%
81723
 
3.5%
9796
 
1.6%
ValueCountFrequency (%)
143
 
< 0.1%
134
 
< 0.1%
1254
 
0.1%
11124
 
0.3%
10383
 
0.8%
9796
 
1.6%
81723
 
3.5%
73061
6.3%
64353
8.9%
55877
12.1%

HighestEducation
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size380.4 KiB
0
34421 
1
10335 
2
 
2924
3
 
998

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
034421
70.7%
110335
 
21.2%
22924
 
6.0%
3998
 
2.1%

Length

2022-02-14T17:52:45.638664image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-14T17:52:45.736694image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
034421
70.7%
110335
 
21.2%
22924
 
6.0%
3998
 
2.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

SexofChild
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size380.4 KiB
1
24943 
2
23735 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
124943
51.2%
223735
48.8%

Length

2022-02-14T17:52:45.846798image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-14T17:52:45.945251image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
124943
51.2%
223735
48.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

childSize
Real number (ℝ≥0)

MISSING

Distinct7
Distinct (%)< 0.1%
Missing5719
Missing (%)11.7%
Infinite0
Infinite (%)0.0%
Mean3.048720873
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.4 KiB
2022-02-14T17:52:46.027252image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.313379059
Coefficient of variation (CV)0.4307967548
Kurtosis0.4629353603
Mean3.048720873
Median Absolute Deviation (MAD)1
Skewness0.3701592213
Sum130970
Variance1.724964552
MonotonicityNot monotonic
2022-02-14T17:52:46.156255image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
316773
34.5%
57202
14.8%
26720
13.8%
16341
 
13.0%
45650
 
11.6%
8197
 
0.4%
976
 
0.2%
(Missing)5719
 
11.7%
ValueCountFrequency (%)
16341
 
13.0%
26720
13.8%
316773
34.5%
45650
 
11.6%
57202
14.8%
8197
 
0.4%
976
 
0.2%
ValueCountFrequency (%)
976
 
0.2%
8197
 
0.4%
57202
14.8%
45650
 
11.6%
316773
34.5%
26720
13.8%
16341
 
13.0%

WealthQuantile
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5
Distinct (%)< 0.1%
Missing10873
Missing (%)22.3%
Memory size380.4 KiB
1.0
12085 
5.0
7472 
2.0
6710 
3.0
5962 
4.0
5576 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row3.0
3rd row3.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
1.012085
24.8%
5.07472
15.3%
2.06710
13.8%
3.05962
12.2%
4.05576
11.5%
(Missing)10873
22.3%

Length

2022-02-14T17:52:46.320572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-14T17:52:46.471557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.012085
32.0%
5.07472
19.8%
2.06710
17.7%
3.05962
15.8%
4.05576
14.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

prev_termin_pregnancy
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing5719
Missing (%)11.7%
Memory size380.4 KiB
0.0
38585 
1.0
4366 
9.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.038585
79.3%
1.04366
 
9.0%
9.08
 
< 0.1%
(Missing)5719
 
11.7%

Length

2022-02-14T17:52:46.672542image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-14T17:52:46.769543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.038585
89.8%
1.04366
 
10.2%
9.08
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Year
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size380.4 KiB
3
11654 
1
10873 
4
10571 
2
9861 
5
5719 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
311654
23.9%
110873
22.3%
410571
21.7%
29861
20.3%
55719
11.7%

Length

2022-02-14T17:52:46.884542image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-14T17:52:46.981558image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
311654
23.9%
110873
22.3%
410571
21.7%
29861
20.3%
55719
11.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Toiletfascility
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing1323
Missing (%)2.7%
Memory size380.4 KiB
0.0
39770 
1.0
7585 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.039770
81.7%
1.07585
 
15.6%
(Missing)1323
 
2.7%

Length

2022-02-14T17:52:47.098184image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-14T17:52:47.177972image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.039770
84.0%
1.07585
 
16.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HandwashFascility
Categorical

MISSING

Distinct2
Distinct (%)< 0.1%
Missing9232
Missing (%)19.0%
Memory size380.4 KiB
1.0
27076 
0.0
12370 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.027076
55.6%
0.012370
25.4%
(Missing)9232
 
19.0%

Length

2022-02-14T17:52:47.256119image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-14T17:52:47.356895image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.027076
68.6%
0.012370
31.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Place_of_delivery
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing6389
Missing (%)13.1%
Memory size380.4 KiB
0.0
35435 
1.0
6854 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.035435
72.8%
1.06854
 
14.1%
(Missing)6389
 
13.1%

Length

2022-02-14T17:52:47.451923image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-14T17:52:47.544372image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.035435
83.8%
1.06854
 
16.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size380.4 KiB
0.0
39197 
1.0
9481 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.039197
80.5%
1.09481
 
19.5%

Length

2022-02-14T17:52:47.609951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-14T17:52:47.688126image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.039197
80.5%
1.09481
 
19.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Mediaexposure
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing5458
Missing (%)11.2%
Memory size380.4 KiB
0.0
27332 
1.0
15888 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.027332
56.1%
1.015888
32.6%
(Missing)5458
 
11.2%

Length

2022-02-14T17:52:47.750620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-14T17:52:47.844152image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.027332
63.2%
1.015888
36.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

age
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct60
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.39631867
Minimum0
Maximum59
Zeros2259
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size380.4 KiB
2022-02-14T17:52:47.944152image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q111
median27
Q343
95-th percentile56
Maximum59
Range59
Interquartile range (IQR)32

Descriptive statistics

Standard deviation17.98561501
Coefficient of variation (CV)0.6564975109
Kurtosis-1.269167207
Mean27.39631867
Median Absolute Deviation (MAD)16
Skewness0.06578261135
Sum1333598
Variance323.4823473
MonotonicityNot monotonic
2022-02-14T17:52:48.109108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02259
 
4.6%
11158
 
2.4%
121081
 
2.2%
61076
 
2.2%
21052
 
2.2%
241047
 
2.2%
31024
 
2.1%
25961
 
2.0%
36942
 
1.9%
49936
 
1.9%
Other values (50)37142
76.3%
ValueCountFrequency (%)
02259
4.6%
11158
2.4%
21052
2.2%
31024
2.1%
4924
1.9%
5905
1.9%
61076
2.2%
7910
1.9%
8840
 
1.7%
9808
 
1.7%
ValueCountFrequency (%)
59615
1.3%
58566
1.2%
57605
1.2%
56700
1.4%
55654
1.3%
54764
1.6%
53808
1.7%
52872
1.8%
51916
1.9%
50899
1.8%

dead_alive
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size380.4 KiB
1
44690 
0
 
3988

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
144690
91.8%
03988
 
8.2%

Length

2022-02-14T17:52:48.255111image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-14T17:52:48.372093image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
144690
91.8%
03988
 
8.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-02-14T17:52:40.110028image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:29.047853image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:30.755764image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:32.646228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:34.135858image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:35.373996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:36.761734image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:38.566389image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:40.278754image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:29.264055image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:30.936768image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:32.839226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:34.268968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:35.532590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:36.941772image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:38.754525image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:40.428237image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:29.479278image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:31.114732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:33.000680image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:34.413954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:35.679876image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:37.108688image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:38.944528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:40.634633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:29.706438image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:31.302732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:33.162997image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:34.586432image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:35.891093image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:37.301421image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:39.132558image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:40.786371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:29.984440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:31.733230image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:33.321064image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:34.741992image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:36.069683image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:37.565387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:39.311013image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:40.949105image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:30.174436image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:31.989230image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:33.474555image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:34.899559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:36.206206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:37.864388image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:39.463685image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:41.099867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:30.378438image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:32.198230image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:33.636621image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:35.054848image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:36.369393image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:38.097387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:39.786190image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:41.252692image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:30.578939image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:32.415262image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:33.972001image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:35.238468image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:36.589732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:38.362387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-14T17:52:39.946970image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-02-14T17:52:48.476079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-14T17:52:49.033374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-14T17:52:49.349121image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-14T17:52:49.706327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-02-14T17:52:49.977210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-02-14T17:52:41.510526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-02-14T17:52:42.174495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-02-14T17:52:42.659533image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-02-14T17:52:42.927981image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

RegionRural_UrbanNo_of_Children_under_5Age_at_1st_BirthBirthOrderPrec.BirthIntervalTotalChildrenHighestEducationSexofChildchildSizeWealthQuantileprev_termin_pregnancyYearToiletfascilityHandwashFascilityPlace_of_deliveryContraceptive_useMediaexposureagedead_alive
0121231NaN1115.0NaN0.010.01.00.00.00.025.01
112216828.07015.0NaN0.010.0NaN0.00.00.012.01
212216737.07023.0NaN0.010.0NaN0.00.00.040.01
312215726.05021.0NaN0.010.0NaN0.00.00.016.01
412215629.05013.0NaN0.010.0NaN0.00.00.042.01
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